We can train a neural network to learn the function \(f\) . The simplest way to do this is using a representative training set of noisy/clean image pairs and training a CNN to map noisy images to their clean counterparts [1]}. Surprisingly, this also works for noisy/noisy image pairs, and with some mild assumptions if we train a neural network to map different noisy realizations of the same image to each other
\(  f(\mathbf {s} + \mathbf {n}_1) \rightarrow \mathbf {s} + \mathbf {n}_2, \) 
